ahajournals.org

Abstract 4345167: A Contemporary Machine Learning-Based Risk Stratification for Mortality and Hospitalization in Heart Failure with Preserved Ejection Fraction Using Multimodal ...

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous condition with high morbidity and mortality. Risk stratification of patients with HFpEF is important for advancing ...
C&EN

Machine Learning-Based Prediction Model for the Stretching-Induced II–I Phase Transition of Polybutene-1

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsapm.5c02453. Engineering stress–strain curve of polybutene-1 ...
IEEE

Machine Learning Techniques to Improve the Cognitive Workload Classification Using Multimodal Sensors’ Data

Abstract: Using machine learning applied to multimodal physiological data allows the classification of cognitive workload (low, moderate, or high load) during task performance. However, current ...
Frontiers

Integrating multimodal ultrasound imaging and machine learning for predicting luminal and non-luminal breast cancer subtypes

Rationale and Objectives: Breast cancer molecular subtypes significantly influence treatment outcomes and prognoses, necessitating precise differentiation to tailor individualized therapies. This ...
Scientific Research Publishing

Sorour, S.S., Saleh, C.A. and Shazly, M. (2024) A Review on Machine Learning Implementation for Predicting and Optimizing the Mechanical Behaviour of Laminated Fiber-Reinforced ...

ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...
cmu.edu

Chemists Can Discover New Materials More Quickly With AI

Everyday items like car tires, plastic bags and foam cushions come from materials called polymers that can take years to develop and test. Researchers at Carnegie Mellon University and the University ...
eLife

Comprehensive Neural Representations of Naturalistic Stimuli through Multimodal Deep Learning

State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China The study leverages a multimodal machine learning ...
Frontiers

Application and validation of the machine learning-based multimodal radiomics model for preoperative prediction of lateral lymph node metastasis in papillary thyroid carcinoma

Background: Papillary thyroid carcinoma (PTC) frequently develops lateral lymph node metastasis (LLNM) in 12.6%-32.8% of patients, increasing recurrence risk and mortality. Current diagnostic methods ...
Scientific Research Publishing

Predicting Antidepressant Treatment Response Using Machine Learning: A Multimodal Analysis of Clinical and Genetic Data ()

1 Department of Neuroscience, Institute of Psychopathology, Rome, Italy. 2 Department of Computer Engineering (AI), University of Genova, Genova, Italy. Accurately predicting individual responses to ...
ascopubs.org

Deep-Learning Model for Real-Time Prediction of Recurrence in Early-Stage Non–Small Cell Lung Cancer: A Multimodal Approach (RADAR CARE Study)

Physiologically Based Pharmacokinetic Model to Assess the Drug-Drug-Gene Interaction Potential of Belzutifan in Combination With Cyclin-Dependent Kinase 4/6 Inhibitors A total of 14,177 patients were ...